class_docstr: This is the matplotlib based GUI for a single analysis
  It assumes the analysis is simple image data (this could be abstracted)
docstr: Set the image indexes to be composited. These indexes are
  offsets in the data (image) dimension
docstr_tok: ["Set","the","image","indexes","to","be","composited",".","These","indexes","are","offsets","in","the","data","(","image",")","dimension"]
code: def set_image_indexes(self, indexes):
      """Set the image indexes to be composited. These indexes are
      offsets in the data (image) dimension"""
      self.composite_digit = np.zeros(
          (self.image_dimensions[0] * self.image_dimensions[1],)
      )
      if len(indexes) > 0:
          for i in indexes:
              self.composite_digit = np.add(
                  self.composite_digit, self.data[i, :]
              )
          self.composite_digit = self.composite_digit / len(indexes)
      digit = np.reshape(self.composite_digit, self.image_dimensions)
      self.display_img.set_array(digit)
      self.display_img.set_clim(
          vmin=digit.min().min(), vmax=digit.max().max()
      )
      self.force_refresh()
  
file_path: nptsne/demos/ExtendedHSNEDemo/CompositeImageViewer.py
imports: import numpy
  import matplotlib.pyplot
  import matplotlib.style
  from matplotlib.backends.backend_qt5agg import FigureCanvas
  from matplotlib.figure import Figure
label_desc: Use image node_id 3 for calling the CompositeImageViewer obj's underlying member method code with example usage: obj.set_image_indexes(indexes) without return types